Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the behavioral disclosure burden. It discloses the core metrics returned and implies a read-only operation, but it does not mention permissions, data scoping, or operational constraints beyond 'per playbook'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, well-structured sentence front-loads the key metrics and ends with a memorable purpose clause. Every word earns its place; no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and zero parameters, the description provides sufficient context for a simple stats tool. It clearly states the per-playbook scope and analytical intent, though it could slightly benefit from clarifying whether results are global or filtered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema already covers all parameter semantics completely. The description adds useful context about what the metrics mean but needs no parameter-level explanation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides open counts and suggested-question click counts per playbook, with an explicit use-case ('find copy nobody clicks'). This is a specific, distinctive resource and metric set that separates it from sibling tools like usage_stats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'find copy nobody clicks' gives a clear analytical context for when to use this tool. It does not name alternative tools or exclusions, but it gives enough guidance to infer intended use over general stats tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.